Impact of Credit Management on the Financial Performance of Banks: A Case Study of Canadian Banks
Bibliographic record
Abstract
Credit is of a sensitive disposition not to be treated with utmost vigilance in any\norganization especially in banks which the circumstance is more significant. The aim\nof this study is to investigate the impact of credit management on the financial\nperformance of banks. Panel data analysis was used to analyze the secondary data\ncollected for 8 Canadian banks over the period of 16 years (2000-2015). In this\nstudy, return on assets (ROA) and return on equity (ROE) are used as a measure of\nbanks‟ financial performance whereas non-performing loan ratio (NPLR), loan loss\nprovision ratio (LLPR), loans to deposit ratio (LTDR), loans to asset ratio (LTAR),\ncost per loan asset ratio (CLAR) and total debt to total asset ratio (TDTAR) were\nused as proxies for credit risk. It was found that NPLR, LLPR, LTDR and CLAR\nwere all statistically significant and inversely related to banks‟ financial performance\n(ROA) whereas LTAR was statistically significant and positively related to ROA. On\nthe other hand, NPLR and LLPR were statistically significant and inversely related to\nROE, while LTAR was positively related but LTDR, CLAR and TDTAR were all\nstatistically insignificant. On the basis of the findings, it shows credit risk has a\nnegative influence on financial performance of banks thereby saying good credit\nmanagement is of utmost importance to banks. Therefore, banks need credit to\nsurvive and hence adequate attention needs to be paid to credit administration in\nbanks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".